Efecto de los sustitutos de radiación neta en la estimación de la evapotranspiración del maíz y la soja mediante métodos de aprendizaje automático

Contenido principal del artículo

Virginia Venturini
Elisabet Walker
Diana Carolina Fonnegra Mora
Gianfranco Fagioli

Resumen

La estimación precisa de la evapotranspiración (ET) es esencial para gestionar agua en cultivos, pero no es una tarea fácil. Las metodologías empíricas de ET requieren mediciones precisas de la radiación neta (Rn) para obtener resultados confiables. Sin embargo, estas mediciones no son rutinarias en las estaciones meteorológicas. Este trabajo exploró el uso de aprendizaje automático para estimar la ET diaria con dos sustitutos de Rn: la radiación solar extraterrestre (Ra) y la Rn modelada (RnM). Se utilizó Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB) y Multilayer Perceptron (MLP) para modelar observaciones de FLUXNET. Adaptive Boosting brindó los mejores resultados con observaciones de Rn (RnO), con un valor para la raíz del error cuadrático medio de aproximadamente el 16 % de Rn medio observado. La Rn resultante (AB RnM) se utilizó para modelar la ET, usando RnO, AB RnM y Ra, junto a variables meteorológicas y el índice NDVI. Los métodos evaluados estimaron adecuadamente la ET, arrojando errores similares a los obtenidos con RnO, cuando se contrastan con las observaciones de ET. Estos resultados demuestran que AB y KR son aplicables con datos rutinarios meteorológicos y de satélite para estimar la ET.

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Venturini, V. ., Walker, E., Fonnegra Mora, D. C., & Fagioli, G. (2022). Efecto de los sustitutos de radiación neta en la estimación de la evapotranspiración del maíz y la soja mediante métodos de aprendizaje automático. AgriScientia, 39(2), 1–17. https://doi.org/10.31047/1668.298x.v39.n2.37104
Sección
Artículos

Citas

Alizamir, M., Kim, S., Kisi, O., and Zounemat-Kermani, M. (2020). A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. Energy, 197, 117239. https://doi.org/10.1016/j.energy.2020.117239

Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO Rome, 300(9), D05109.

Anguita, D., Ridella, S., and Rivieccio, F. (2005, July). K-fold generalization capability assessment for support vector classifiers. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2 (pp. 855-858). IEEE. https://doi.org /10.1109/IJCNN.2005.1555964

Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214, 1-12. https://doi.org/10.1038/sdata.2018.214

Bisht, G., Venturini, V., Islam, S., and Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days. Remote Sensing of Environment, 97(1), 52-67. https://doi.org/10.1016/j.rse.2005.03.014

Carter, C. and Liang, S. (2019). Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 78, 86-92. https://doi.org/10.1016/j.jag.2019.01.020

Chen, J., He, T., Jiang, B., and Liang, S. (2020). Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data. Remote Sensing of Environment, 245, 111842. https://doi.org/10.1016/j.rse.2020.111842

Chen, Z., Zhu, Z., Jiang, H., and Sun, S. (2020). Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. Journal of Hydrology, 591, 125286. https://doi.org/10.1016/j.jhydrol.2020.125286

Darnell, S. J., Page, D., and Mitchell, J. C. (2007). An automated decision‐tree approach to predicting protein interaction hot spots. Proteins: Structure, Function, and Bioinformatics, 68(4), 813-823. https://doi.org/10.1002/prot.21474

Exterkate, P., Groenen, P. J. F., Heij, C., and van Dijk, D. (2016). Nonlinear forecasting with many predictors using kernel ridge regression. International Journal of Forecasting, 32(3), 736–753. https://doi.org/10.1016/j.ijforecast.2015.11.017

Fan, J., Zheng, J., Wu, L., and Zhang, F. (2021). Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models. Agricultural Water Management, 245, 106547. https://doi.org/10.1016/j.agwat.2020.106547

Food and Agriculture Organization of the United Nations (FAO), International Institute for Applied Systems Analysis (IIASA), ISRIC-World Soil Information, Institute of Soil Science – Chinese Academy of Sciences (ISSCAS), and Joint Research Centre of the European Commission (JRC). (2012).

Harmonized World Soil Database (version 1.2) [Software]. FAO, Rome, Italy and IIASA, Laxenburg, Austria. http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/index.html?sb=1

García, G. A., Venturini, V., Brogioni, M., Walker, E., and Rodríguez, L. (2019). Soil moisture estimation over flat lands in the Argentinian Pampas region using Sentinel-1A data and non-parametric methods. International Journal of Remote Sensing, 40(10), 3689-3720. https://doi.org/10.1080/01431161.2018.1552813

Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms — A comparative study. Agricultural Water Management, 217, 303-315. https://doi.org/10.1016/j.agwat.2019.03.015

Hargreaves, G. L., Hargreaves, G. H., and Riley, J. P. (1985). Irrigation water requirements for Senegal River basin. Journal of Irrigation and Drainage Engineering, 111(3), 265-275. https://doi.org/10.1061/(ASCE)0733-9437(1985)111:3(265)

Hofmann, T., Schölkopf, B., and Smola, A. J. (2008). Kernel methods in machine learning. The Annals of Statistics, 36(3), 1171-1220. https://doi.org/10.1214/009053607000000677

Jain, S. K., Nayak, P. C., and Sudheer, K. P. (2008). Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes: An International Journal, 22(13), 2225-2234. https://doi.org/10.1002/hyp.6819

Jiang, B., Zhang, Y., Liang, S., Zhang, X., and Xiao, Z. (2014). Surface daytime net radiation estimation using artificial neural networks. Remote Sensing, 6(11), 11031-11050. https://doi.org/10.3390/rs61111031

Kim, H., Parinussa, R., Konings, A. G., Wagner, W., Cosh, M. H., Lakshmi, V., Zohaib, M. and Choi, M. (2018). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. https://doi.org/10.1016/j.rse.2017.10.026

Kumar, M., Raghuwanshi, N. S., and Singh, R. (2011). Artificial neural networks approach in evapotranspiration modeling: a review. Irrigation Science, 29(1), 11-25. https://doi.org/10.1007/s00271-010-0230-8

Llasat, M. C. and Snyder, R. L. (1998). Data error effects on net radiation and evapotranspiration estimation. Agricultural and Forest Meteorology, 91(3-4), 209-221. https://doi.org/10.1016/S0168-1923(98)00070-7

Majidi, M., Alizadeh, A., Vazifedoust, M., Farid, A., and Ahmadi, T. (2015). Analysis of the effect of missing weather data on estimating daily reference evapotranspiration under different climatic conditions. Water Resources Management, 29(7), 2107-2124. https://doi.org/10.1007/s11269-014-0782-0

Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J. (2011). Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15(2), 453-469. https://doi.org/10.5194/hess-15-453-2011

Mokhtari, A., Noory, H., and Vazifedoust, M. (2018). Performance of different surface incoming solar radiation models and their impacts on reference evapotranspiration. Water Resources Management, 32(9), 3053-3070. https://doi.org/10.1007/s11269-018-1974-9

Nourani, V., Tajbakhsh, A. D., and Molajou, A. (2019). Data mining based on wavelet and decision tree for rainfall-runoff simulation. Hydrology Research, 50(1), 75-84. https://doi.org/10.2166/nh.2018.049

Ojo, O. S., Adeyemi, B., and Oluleye, D. O. (2021). Artificial neural network models for prediction of net radiation over a tropical region. Neural Computing and Applications, 33(12), 6865-6877. https://doi.org/10.1007/s00521-020-05463-9

Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193(1032), 120-145. https://doi.org/10.1098/rspa.1948.0037

Priestley, C. H. B. and Taylor, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review, 100(2), 81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2

Purdy, A. J., Fisher, J. B., Goulden, M. L., Colliander, A., Halverson, G., Tu, K., and Famiglietti, J. S. (2018). SMAP soil moisture improves global evapotranspiration. Remote Sensing of Environment, 219, 1-14. https://doi.org/10.1016/j.rse.2018.09.023

Qiu, R., Liu, C., Cui, N., Wu, Y., Wang, Z., and Li, G. (2019). Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system. Agricultural Water Management, 224, 105755. https://doi.org/10.1016/j.agwat.2019.105755

Saunders, C., Gammerman, A., and Vovk, V. (1998). Ridge Regression Learning Algorithm in Dual Variables. In Proceeding 15th International Conference Machine Learning (pp. 1–7).

Schwertman, N. C., Owens, M. A., and Adnan, R. (2004). A simple more general boxplot method for identifying outliers. Computational Statistics and Data Analysis, 47(1), 165-174. https://doi.org/10.1016/j.csda.2003.10.012

Si, Z., Yu, Y., Yang, M., and Li, P. (2020). Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks. IEEE Transactions on Industry Applications, 57(1), 5-16. https://doi.org/10.1109/TIA.2020.3028558

Shirazi, M. A., Boersma, L., and Hart, J. W. (1988). A unifying quantitative analysis of soil texture: improvement of precision and extension of scale. Soil Science Society of America Journal, 52(1), 181-190. https://doi.org/10.2136/sssaj1988.03615995005200010032x

Tang, D., Feng, Y., Gong, D., Hao, W., and Cui, N. (2018). Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands. Computers and Electronics in Agriculture, 152, 375-384. https://doi.org/10.1016/j.compag.2018.07.029

Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192. https://doi.org/10.1029/2000JD900719

Tikhamarine, Y., Malik, A., Pandey, K., Sammen, S. S., Souag-Gamane, D., Heddam, S., and Kisi, O. (2020a). Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm. Environmental Monitoring and Assessment, 192(11), 1-19. https://doi.org/10.1007/s10661-020-08659-7

Tikhamarine, Y., Malik, A., Souag-Gamane, D., and Kisi, O. (2020b). Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environmental Science and Pollution Research, 27(24), 30001-30019. https://doi.org/10.1007/s11356-020-08792-3

Trnka, M., Eitzinger, J., Kapler, P., Dubrovský, M., Semerádová, D., Žalud, Z., and Formayer, H. (2007). Effect of estimated daily global solar radiation data on the results of crop growth models. Sensors, 7(10), 2330-2362. https://doi.org/10.3390/s7102330

Vapnik, V. (1999). The nature of statistical learning theory (2nd Ed.). Springer.

Walker, E. and Venturini, V. (2019). Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine. Remote Sensing Letters, 10(10), 929-938. https://doi.org/10.1080/2150704X.2019.1633487

Wang, Y., Jiang, B., Liang, S., Wang, D., He, T., Wang, Q., Zhao, X., and Xu, J. (2019). Surface Shortwave net radiation estimation from Landsat TM/ETM+ data using four machine learning algorithms. Remote Sensing, 11(23), 2847. https://doi.org/10.3390/rs11232847

Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zou, Z.-H., Steinbach, M., Hand, D. J., and Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37. https://doi.org/10.1007/s10115-007-0114-2

Xu, M., Watanachaturaporn, P., Varshney, P. K., and Arora, M. K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322-336. https://doi.org/10.1016/j.rse.2005.05.008

Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang, X., and Zhao, C. (2019). Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. Journal of Hydrology, 578, 124105. https://doi.org/10.1016/j.jhydrol.2019.124105

Yadav, A. K. and Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772-781. https://doi.org/10.1016/j.rser.2013.08.055

Yamaç, S. S. and Todorovic, M. (2020). Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management, 228, 105875. https://doi.org/10.1016/j.agwat.2019.105875

You, Y., Demmel, J., Hsieh, C. J., and Vuduc, R. (2018, June). Accurate, fast and scalable kernel ridge regression on parallel and distributed systems. In Proceedings of the 2018 International Conference on Supercomputing (pp. 307-317). https://doi.org/10.1145/3205289.3205290

Zhang, Y., Duchi, J., and Wainwright, M. (2013, June). Divide and conquer kernel ridge regression. In Conference on learning theory (pp. 592-617). PMLR.

Zhang, X., Treitz, P. M., Chen, D., Quan, C., Shi, L., and Li, X. (2017). Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62, 201-214. https://doi.org/10.1016/j.jag.2017.06.010

Zhang, Y., Qin, X., Li, X., Zhao, J., and Liu, Y. (2020). Estimation of Shortwave Solar Radiation on Clear-Sky Days for a Valley Glacier with Sentinel-2 Time Series. Remote Sensing, 12(6), 927. https://doi.org/10.3390/rs12060927